A needlet-based approach to the full-sky data analysis

TL;DR

This paper explores the application of needlets, a spherical wavelet technique, to analyze full-sky cosmic-ray and gamma-ray data, demonstrating their effectiveness in background estimation and point-source detection compared to standard methods.

Contribution

It introduces the use of needlets for cosmic-ray physics data analysis, showing their potential for unbiased background estimation and improved point-source detection.

Findings

Needlets effectively estimate background contributions in simulated datasets.

Needlets outperform standard analysis techniques in point-source detection.

Application to satellite and EAS array simulations demonstrates versatility.

Abstract

In cosmic-ray physics, large field of view experiments are triggered by a number of signals laying on different angular scales: point-like and extended gamma-ray sources, diffuse emissions, as well as large and intermediate scale cosmic-ray anisotropies. The separation of all these contributions is crucial, mostly when they overlap with each other. Needlets are a form of spherical wavelets that have recently drawn a lot of attention in the cosmological literature, especially in connection with the analysis of CMB data. Needlets enjoy a number of important statistical and numerical properties which suggest that they can be very effective in handling cosmic-ray and gamma-ray data analysis. An application of needlets to astroparticle physics is shown here. In particular, light will be thrown on how useful they might be for estimating background and foreground contributions. Since such an estimation is expected to be optimal or nearly-optimal in a well-defined mathematical sense, needlets turn out to be a powerful method for unbiased point-source detections. In this paper needlets were applied to two distinct simulated datasets, for satellite and EAS array experiments, both large field of view telescopes. Results will be compared to those achievable with standard analysis tecniques in any of these cases.